pytorch - 💡(How to fix) Fix [CUDA] `test_mm_concat_cuda` fails flakily on GB200 [1 participants]

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pytorch/pytorch#181770Fetched 2026-04-29 06:11:03
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Error Message

====================================================================== ERROR: test_mm_concat_cuda (main.FreezingGpuTests.test_mm_concat_cuda)

Traceback (most recent call last): File "/usr/local/lib/python3.12/dist-packages/torch/testing/_internal/common_utils.py", line 3514, in wrapper method(*args, **kwargs) File "/opt/pytorch/pytorch/test/inductor/test_torchinductor.py", line 16554, in new_test return value(self) ^^^^^^^^^^^ File "/usr/lib/python3.12/contextlib.py", line 81, in inner return func(*args, **kwds) ^^^^^^^^^^^^^^^^^^^ File "/opt/pytorch/pytorch/test/inductor/test_inductor_freezing.py", line 354, in test_mm_concat ).run(code[0]) ^^^^^^^^^^^^ RuntimeError: Expected to not find "triton.jit" but found it min_elem_per_thread=0 ) @triton.jit

def triton_poi_fused_mm_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
   xnumel = 144
From CHECK-NOT: triton.jit


To execute this test, run the following from the base repo dir:
   python test/inductor/test_inductor_freezing.py FreezingGpuTests.test_mm_concat_cuda

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0

----------------------------------------------------------------------

Fix Action

Fix / Workaround

CPU:
Architecture: aarch64
CPU op-mode(s): 64-bit
Byte Order: Little Endian
CPU(s): 144
On-line CPU(s) list: 0-143
Vendor ID: ARM
BIOS Vendor ID: NVIDIA
Model name: Neoverse-V2
BIOS Model name: Grace A02P 692-2G548-0001-0B0 CPU @ 3.3GHz
BIOS CPU family: 258
Model: 0
Thread(s) per core: 1
Core(s) per socket: 72
Socket(s): 2
Stepping: r0p0
Frequency boost: disabled
CPU(s) scaling MHz: 100%
CPU max MHz: 3420.0000
CPU min MHz: 81.0000
BogoMIPS: 2000.00 Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti L1d cache: 9 MiB (144 instances) L1i cache: 9 MiB (144 instances) L2 cache: 144 MiB (144 instances) L3 cache: 228 MiB (2 instances) NUMA node(s): 34 NUMA node0 CPU(s): 0-71 NUMA node1 CPU(s): 72-143 NUMA node2 CPU(s):
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Vulnerability Gather data sampling: Not affected Vulnerability Ghostwrite: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Not affected Vulnerability Spectre v1: Mitigation; __user pointer sanitization Vulnerability Spectre v2: Mitigation; CSV2, but not BHB Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Code Example

======================================================================
ERROR: test_mm_concat_cuda (__main__.FreezingGpuTests.test_mm_concat_cuda)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/torch/testing/_internal/common_utils.py", line 3514, in wrapper
    method(*args, **kwargs)
  File "/opt/pytorch/pytorch/test/inductor/test_torchinductor.py", line 16554, in new_test
    return value(self)
           ^^^^^^^^^^^
  File "/usr/lib/python3.12/contextlib.py", line 81, in inner
    return func(*args, **kwds)
           ^^^^^^^^^^^^^^^^^^^
  File "/opt/pytorch/pytorch/test/inductor/test_inductor_freezing.py", line 354, in test_mm_concat
    ).run(code[0])
      ^^^^^^^^^^^^
RuntimeError: Expected to not find "triton.jit" but found it
    min_elem_per_thread=0
)
@triton.jit
 ~~~~~~~~~~ <--- HERE
def triton_poi_fused_mm_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 144
From CHECK-NOT: triton.jit


To execute this test, run the following from the base repo dir:
    python test/inductor/test_inductor_freezing.py FreezingGpuTests.test_mm_concat_cuda

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0

----------------------------------------------------------------------

---

Collecting environment information...                                                                                                                                    
PyTorch version: N/A                                                                                                                                                     
Is debug build: N/A                                                                                                                                                      
CUDA used to build PyTorch: N/A                                                                                                                                          
ROCM used to build PyTorch: N/A                                                                                                                                          
                                                                                                                                                                         
OS: Ubuntu 24.04.4 LTS (aarch64)                                                                                                                                         
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0                                                                                                                     
Clang version: Could not collect                                                                                                                                         
CMake version: version 3.31.6                                                                                                                                            
Libc version: glibc-2.39                                                                                                                                                 
                                                                                                                                                                         
Python version: 3.12.3 (main, Mar  3 2026, 12:15:18) [GCC 13.3.0] (64-bit runtime)                                                                                       
Python platform: Linux-6.14.0-1008-nvidia-64k-aarch64-with-glibc2.39                                                                                                     
Is CUDA available: N/A                                                                                                                                                   
CUDA runtime version: 13.2.78                                                                                                                                            
CUDA_MODULE_LOADING set to: N/A                                                                                                                                          
GPU models and configuration:                                                                                                                                            
GPU 0: NVIDIA GB200                                                                                                                                                      
GPU 1: NVIDIA GB200                                                                                                                                                      
GPU 2: NVIDIA GB200                                                                                                                                                      
GPU 3: NVIDIA GB200                                                                                                                                                      
                                                                                                                                                                         
Nvidia driver version: 580.95.07                                                                                                                                         
cuDNN version: Probably one of the following:                                                                                                                            
/usr/lib/aarch64-linux-gnu/libcudnn.so.9.21.1                                                                                                                            
/usr/lib/aarch64-linux-gnu/libcudnn_adv.so.9.21.1                                                                                                                        
/usr/lib/aarch64-linux-gnu/libcudnn_cnn.so.9.21.1                                                                                                                        
/usr/lib/aarch64-linux-gnu/libcudnn_engines_precompiled.so.9.21.1                                                                                                        
/usr/lib/aarch64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.21.1                                                                                                   
/usr/lib/aarch64-linux-gnu/libcudnn_engines_tensor_ir.so.9.21.1                                                                                                          
/usr/lib/aarch64-linux-gnu/libcudnn_graph.so.9.21.1                                                                                                                      
/usr/lib/aarch64-linux-gnu/libcudnn_heuristic.so.9.21.1                                                                                                                  
/usr/lib/aarch64-linux-gnu/libcudnn_ops.so.9.21.1                                                                                                                        
Is XPU available: N/A                                                                                                                                                    
HIP runtime version: N/A                                                                                                                                                 
MIOpen runtime version: N/A                                                                                                                                              
Is XNNPACK available: N/A                                                                                                                                                
Caching allocator config: N/A                                                                                                                                            
                                                                                                                                                                         
CPU:                                                                                                                                                                     
Architecture:                         aarch64                                                                                                                            
CPU op-mode(s):                       64-bit                                                                                                                             
Byte Order:                           Little Endian                                                                                                                      
CPU(s):                               144                                                                                                                                
On-line CPU(s) list:                  0-143                                                                                                                              
Vendor ID:                            ARM                                                                                                                                
BIOS Vendor ID:                       NVIDIA                                                                                                                             
Model name:                           Neoverse-V2                                                                                                                        
BIOS Model name:                      Grace A02P 692-2G548-0001-0B0 CPU @ 3.3GHz                                                                                         
BIOS CPU family:                      258                                                                                                                                
Model:                                0                                                                                                                                  
Thread(s) per core:                   1                                                                                                                                  
Core(s) per socket:                   72                                                                                                                                 
Socket(s):                            2                                                                                                                                  
Stepping:                             r0p0                                                                                                                               
Frequency boost:                      disabled                                                                                                                           
CPU(s) scaling MHz:                   100%                                                                                                                               
CPU max MHz:                          3420.0000                                                                                                                          
CPU min MHz:                          81.0000                                                                                                                            
BogoMIPS:                             2000.00
Flags:                                fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti
L1d cache:                            9 MiB (144 instances)
L1i cache:                            9 MiB (144 instances)
L2 cache:                             144 MiB (144 instances)
L3 cache:                             228 MiB (2 instances)
NUMA node(s):                         34
NUMA node0 CPU(s):                    0-71
NUMA node1 CPU(s):                    72-143
NUMA node2 CPU(s):                    
NUMA node3 CPU(s):                    
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NUMA node33 CPU(s):                   
Vulnerability Gather data sampling:   Not affected
Vulnerability Ghostwrite:             Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Not affected
Vulnerability Spectre v1:             Mitigation; __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; CSV2, but not BHB
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==2.1.0
[pip3] optree==0.19.0
[pip3] torch==2.13.0a0+0291f960b6
[pip3] torchvision==0.26.0a0+48956e05.nvinternal.main.build.and.test.upstream.tot.and.gb200.only.49666224
[pip3] triton==3.7.0+gitb4e20bb.nv26.5
[pip3] triton_kernels==1.0.0+gitb4e20bb.nv26.5
[conda] Could not collect
RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

python test/inductor/test_inductor_freezing.py FreezingGpuTests.test_mm_concat_cuda is failing roughly 5-10% of the time in isolation on a source build of main on GB200.

======================================================================
ERROR: test_mm_concat_cuda (__main__.FreezingGpuTests.test_mm_concat_cuda)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/dist-packages/torch/testing/_internal/common_utils.py", line 3514, in wrapper
    method(*args, **kwargs)
  File "/opt/pytorch/pytorch/test/inductor/test_torchinductor.py", line 16554, in new_test
    return value(self)
           ^^^^^^^^^^^
  File "/usr/lib/python3.12/contextlib.py", line 81, in inner
    return func(*args, **kwds)
           ^^^^^^^^^^^^^^^^^^^
  File "/opt/pytorch/pytorch/test/inductor/test_inductor_freezing.py", line 354, in test_mm_concat
    ).run(code[0])
      ^^^^^^^^^^^^
RuntimeError: Expected to not find "triton.jit" but found it
    min_elem_per_thread=0
)
@triton.jit
 ~~~~~~~~~~ <--- HERE
def triton_poi_fused_mm_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 144
From CHECK-NOT: triton.jit


To execute this test, run the following from the base repo dir:
    python test/inductor/test_inductor_freezing.py FreezingGpuTests.test_mm_concat_cuda

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0

----------------------------------------------------------------------

Versions

Collecting environment information...                                                                                                                                    
PyTorch version: N/A                                                                                                                                                     
Is debug build: N/A                                                                                                                                                      
CUDA used to build PyTorch: N/A                                                                                                                                          
ROCM used to build PyTorch: N/A                                                                                                                                          
                                                                                                                                                                         
OS: Ubuntu 24.04.4 LTS (aarch64)                                                                                                                                         
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0                                                                                                                     
Clang version: Could not collect                                                                                                                                         
CMake version: version 3.31.6                                                                                                                                            
Libc version: glibc-2.39                                                                                                                                                 
                                                                                                                                                                         
Python version: 3.12.3 (main, Mar  3 2026, 12:15:18) [GCC 13.3.0] (64-bit runtime)                                                                                       
Python platform: Linux-6.14.0-1008-nvidia-64k-aarch64-with-glibc2.39                                                                                                     
Is CUDA available: N/A                                                                                                                                                   
CUDA runtime version: 13.2.78                                                                                                                                            
CUDA_MODULE_LOADING set to: N/A                                                                                                                                          
GPU models and configuration:                                                                                                                                            
GPU 0: NVIDIA GB200                                                                                                                                                      
GPU 1: NVIDIA GB200                                                                                                                                                      
GPU 2: NVIDIA GB200                                                                                                                                                      
GPU 3: NVIDIA GB200                                                                                                                                                      
                                                                                                                                                                         
Nvidia driver version: 580.95.07                                                                                                                                         
cuDNN version: Probably one of the following:                                                                                                                            
/usr/lib/aarch64-linux-gnu/libcudnn.so.9.21.1                                                                                                                            
/usr/lib/aarch64-linux-gnu/libcudnn_adv.so.9.21.1                                                                                                                        
/usr/lib/aarch64-linux-gnu/libcudnn_cnn.so.9.21.1                                                                                                                        
/usr/lib/aarch64-linux-gnu/libcudnn_engines_precompiled.so.9.21.1                                                                                                        
/usr/lib/aarch64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.21.1                                                                                                   
/usr/lib/aarch64-linux-gnu/libcudnn_engines_tensor_ir.so.9.21.1                                                                                                          
/usr/lib/aarch64-linux-gnu/libcudnn_graph.so.9.21.1                                                                                                                      
/usr/lib/aarch64-linux-gnu/libcudnn_heuristic.so.9.21.1                                                                                                                  
/usr/lib/aarch64-linux-gnu/libcudnn_ops.so.9.21.1                                                                                                                        
Is XPU available: N/A                                                                                                                                                    
HIP runtime version: N/A                                                                                                                                                 
MIOpen runtime version: N/A                                                                                                                                              
Is XNNPACK available: N/A                                                                                                                                                
Caching allocator config: N/A                                                                                                                                            
                                                                                                                                                                         
CPU:                                                                                                                                                                     
Architecture:                         aarch64                                                                                                                            
CPU op-mode(s):                       64-bit                                                                                                                             
Byte Order:                           Little Endian                                                                                                                      
CPU(s):                               144                                                                                                                                
On-line CPU(s) list:                  0-143                                                                                                                              
Vendor ID:                            ARM                                                                                                                                
BIOS Vendor ID:                       NVIDIA                                                                                                                             
Model name:                           Neoverse-V2                                                                                                                        
BIOS Model name:                      Grace A02P 692-2G548-0001-0B0 CPU @ 3.3GHz                                                                                         
BIOS CPU family:                      258                                                                                                                                
Model:                                0                                                                                                                                  
Thread(s) per core:                   1                                                                                                                                  
Core(s) per socket:                   72                                                                                                                                 
Socket(s):                            2                                                                                                                                  
Stepping:                             r0p0                                                                                                                               
Frequency boost:                      disabled                                                                                                                           
CPU(s) scaling MHz:                   100%                                                                                                                               
CPU max MHz:                          3420.0000                                                                                                                          
CPU min MHz:                          81.0000                                                                                                                            
BogoMIPS:                             2000.00
Flags:                                fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti
L1d cache:                            9 MiB (144 instances)
L1i cache:                            9 MiB (144 instances)
L2 cache:                             144 MiB (144 instances)
L3 cache:                             228 MiB (2 instances)
NUMA node(s):                         34
NUMA node0 CPU(s):                    0-71
NUMA node1 CPU(s):                    72-143
NUMA node2 CPU(s):                    
NUMA node3 CPU(s):                    
NUMA node4 CPU(s):                    
NUMA node5 CPU(s):                    
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NUMA node30 CPU(s):                   
NUMA node31 CPU(s):                   
NUMA node32 CPU(s):                   
NUMA node33 CPU(s):                   
Vulnerability Gather data sampling:   Not affected
Vulnerability Ghostwrite:             Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Not affected
Vulnerability Spectre v1:             Mitigation; __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; CSV2, but not BHB
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==2.1.0
[pip3] optree==0.19.0
[pip3] torch==2.13.0a0+0291f960b6
[pip3] torchvision==0.26.0a0+48956e05.nvinternal.main.build.and.test.upstream.tot.and.gb200.only.49666224
[pip3] triton==3.7.0+gitb4e20bb.nv26.5
[pip3] triton_kernels==1.0.0+gitb4e20bb.nv26.5
[conda] Could not collect

cc @ptrblck @msaroufim @eqy @jerryzh168 @tinglvv @nWEIdia @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @kadeng @muchulee8 @amjames @aakhundov @coconutruben @jataylo

extent analysis

TL;DR

The test_mm_concat_cuda test is failing intermittently due to an unexpected presence of triton.jit, suggesting a potential issue with the test's expectations or the triton library's behavior.

Guidance

  • Review the test_mm_concat_cuda test to ensure it correctly handles the presence of triton.jit.
  • Verify that the triton library version 3.7.0+gitb4e20bb.nv26.5 is compatible with the PyTorch version 2.13.0a0+0291f960b6.
  • Check the test's runtime environment to ensure that no external factors are influencing the test's behavior.
  • Consider updating the triton library to a newer version or modifying the test to account for the presence of triton.jit.

Example

No code example is provided as the issue is related to a specific test case and library interaction.

Notes

The intermittent nature of the failure suggests that the issue may be related to a race condition or an environmental factor. Further investigation is needed to determine the root cause of the failure.

Recommendation

Apply a workaround by modifying the test_mm_concat_cuda test to account for the presence of triton.jit, as updating the triton library may not be feasible or may introduce other compatibility issues.

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pytorch - 💡(How to fix) Fix [CUDA] `test_mm_concat_cuda` fails flakily on GB200 [1 participants]